Synthesizing Camera Noise Using Generative Adversarial Networks.

We present a technique for synthesizing realistic noise for digital photographs. It can adjust the noise level of an input photograph, either increasing or decreasing it, to match a target ISO level. Our solution learns the mappings among different ISO levels from unpaired data using generative adversarial networks. We demonstrate its effectiveness both quantitatively, using Kullback-Leibler divergence and Kolmogorov-Smirnov test, and qualitatively through a large number of examples. We also demonstrate its practical applicability by using its results to significantly improve the performance of a state-of-the-art trainable denoising method. Our technique should benefit several computer-vision applications that seek robustness to noisy scenarios.